鉴于基于机器视觉的太阳能电池片表面缺陷检测方法具有操作简便、检测精度高的优势，对此类方法所涉及的各个环节进行了综述。首先，对太阳能电池片表面的各种成像方式和常见缺陷类型进行了归纳总结；其次，对现有的检测方法按照数学建模思路的不同进行了分类介绍和对比分析；最后，对内容进行了小结并对太阳能电池片表面缺陷检测方法的后续研究进行了展望。可以看出：基于机器视觉的太阳能电池片表面缺陷检测方法已经取得了较大的发展，但在特征提取算法设计方面仍有改进空间，如基于深度神经网络的特征提取算法。%Considering the advantages of simple operation and high detecting accuracy, all aspects involved in solar cell surface defect detection methods based on machine vision were reviewed in this paper. First of all, the various imaging techniques and common defect types of solar cells surface were summarized. Secondly, the existing detection methods were introduced and compared with each other according to the different idea of mathematical modeling. Finally, a brief summary of this article and perspective of future research are presented. It can be concluded that the solar cell surface defect detection methods based on machine vision have made great progress. However, there is still room for improvement in algorithm design of feature extraction, such as feature extraction algorithm based on deep neural networks.
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